Analysis of 2020-11-03 cell count + lum data

library("diprate")
library("platetools")
library("RColorBrewer")

d <- read.csv("../data/2020-11-03_lum&cell_counts.csv", as.is=TRUE)
d$time <- round(d$time,2)
d$orig.cell.count <- d$cell.count
d$cell.count <- d$orig.cell.count - d$ch2.pos
d$cell.count <- sapply(d$cell.count, function(x) ifelse(x <= 0, 1, x))

Examine 293FT data

First, use a single cell line in control conditions to assess population growth.

a <- d[d$cell.line=="293FT" & d$drug1.conc==0,]

invisible(do.call(plotGC, getGCargs(a)))

Find wells where the cell counts reach a high value.

x <- a[a$well %in% c("F13","G13","H13"),]
invisible(plotGC(x$time,x$cell.count,x$uid))

x <- a[a$well %in% c("F13","G13","H13"),]
invisible(plotGC(x$time,x$RLU,x$uid))

plot(a$cell.count ~ a$RLU)

invisible(do.call(plotGC, getGCargs(a, dat.col = c("time", "RLU", "uid"))))

Get DIP rates from test plate

Will use to assess variation across plate.

b <- d[d$cell.line=="293FT",]

dipr <- lapply(unique(b$uid), function(id) tryCatch(
    {
        findDIP(b[b$uid==id,c('time','cell.count','uid')])
    },error=function(cond) {NA})
)
names(dipr) <- unique(b$uid)

rates <- b[!duplicated(b$uid),]
rates <- rates[,!colnames(rates) %in% c('time','plate.id','image.time','cell.count')]
rates$rate <- sapply(rates$uid, function(id) dipr[[id]]$dip)
rates$rate.95ci <- sapply(rates$uid, function(id) dipr[[id]]$dip.95ci)

platetools::raw_map(data=rates$rate, well=rates$well, plate=384) + scale_fill_gradient2()